1,635 research outputs found
Quality Aware Network for Set to Set Recognition
This paper targets on the problem of set to set recognition, which learns the
metric between two image sets. Images in each set belong to the same identity.
Since images in a set can be complementary, they hopefully lead to higher
accuracy in practical applications. However, the quality of each sample cannot
be guaranteed, and samples with poor quality will hurt the metric. In this
paper, the quality aware network (QAN) is proposed to confront this problem,
where the quality of each sample can be automatically learned although such
information is not explicitly provided in the training stage. The network has
two branches, where the first branch extracts appearance feature embedding for
each sample and the other branch predicts quality score for each sample.
Features and quality scores of all samples in a set are then aggregated to
generate the final feature embedding. We show that the two branches can be
trained in an end-to-end manner given only the set-level identity annotation.
Analysis on gradient spread of this mechanism indicates that the quality
learned by the network is beneficial to set-to-set recognition and simplifies
the distribution that the network needs to fit. Experiments on both face
verification and person re-identification show advantages of the proposed QAN.
The source code and network structure can be downloaded at
https://github.com/sciencefans/Quality-Aware-Network.Comment: Accepted at CVPR 201
A Semiblind Two-Way Training Method for Discriminatory Channel Estimation in MIMO Systems
Discriminatory channel estimation (DCE) is a recently developed strategy to
enlarge the performance difference between a legitimate receiver (LR) and an
unauthorized receiver (UR) in a multiple-input multiple-output (MIMO) wireless
system. Specifically, it makes use of properly designed training signals to
degrade channel estimation at the UR which in turn limits the UR's
eavesdropping capability during data transmission. In this paper, we propose a
new two-way training scheme for DCE through exploiting a whitening-rotation
(WR) based semiblind method. To characterize the performance of DCE, a
closed-form expression of the normalized mean squared error (NMSE) of the
channel estimation is derived for both the LR and the UR. Furthermore, the
developed analytical results on NMSE are utilized to perform optimal power
allocation between the training signal and artificial noise (AN). The
advantages of our proposed DCE scheme are two folds: 1) compared to the
existing DCE scheme based on the linear minimum mean square error (LMMSE)
channel estimator, the proposed scheme adopts a semiblind approach and achieves
better DCE performance; 2) the proposed scheme is robust against active
eavesdropping with the pilot contamination attack, whereas the existing scheme
fails under such an attack.Comment: accepted for publication in IEEE Transactions on Communication
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition
Noisy Student Training (NST) has recently demonstrated extremely strong
performance in Automatic Speech Recognition (ASR). In this paper, we propose a
data selection strategy named LM Filter to improve the performances of NST on
non-target domain data in ASR tasks. Hypothesis with and without Language Model
are generated and CER differences between them are utilized as a filter
threshold. Results reveal that significant improvements of 10.4% compared with
no data filtering baselines. We can achieve 3.31% CER in AISHELL-1 test set,
which is best result from our knowledge without any other supervised data. We
also perform evaluations on supervised 1000 hour AISHELL-2 dataset and
competitive results of 4.72% CER can be achieved
Sorted L1/L2 Minimization for Sparse Signal Recovery
This paper introduces a novel approach for recovering sparse signals using
sorted L1/L2 minimization. The proposed method assigns higher weights to
indices with smaller absolute values and lower weights to larger values,
effectively preserving the most significant contributions to the signal while
promoting sparsity. We present models for both noise-free and noisy scenarios,
and rigorously prove the existence of solutions for each case. To solve these
models, we adopt a linearization approach inspired by the difference of convex
functions algorithm. Our experimental results demonstrate the superiority of
our method over state-of-the-art approaches in sparse signal recovery across
various circumstances, particularly in support detection.Comment: 23 page
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